Technical Specifications

GPT-5 Nano

GPT-5 Nano is the most compact and efficient entry in the GPT-5 family, engineered for environments where low latency and high throughput are the primary engineering constraints. Unlike its larger counterparts, Nano is specifically architected to facilitate rapid, real-time interactions and lightweight agentic tasks. It functions as part of a unified routing system that dynamically allocates compute resources, allowing the model to serve as a fast-response engine for routine classifications, basic summarizations, and high-frequency API calls while maintaining the instruction-following precision characteristic of the GPT-5 lineage.

Technically, the model utilizes a dense transformer architecture optimized for edge-ready deployment and cost-effective scaling. It incorporates variable reasoning effort levels, minimal, low, medium, and high, enabling developers to tune the balance between inference speed and cognitive depth per request. This flexibility is supported by an expanded context window of 400,000 tokens, which allows the model to process extensive document sets or lengthy conversation histories despite its smaller parameter footprint. The architecture also integrates multi-modal input support, enabling the processing of both text and image data natively within the same inference pass.

From an operational perspective, GPT-5 Nano is positioned as a replacement for previous-generation lightweight models, offering a significantly lower price point for high-volume workloads. It is optimized for integration into developer tools, mobile applications, and low-power devices where resource efficiency is mandatory. By prioritizing throughput and reducing the frequency of hallucinations through refined training on high-fidelity datasets, the model provides a reliable foundation for building responsive AI services that require consistent performance across large-scale deployments.